Further, we employed SHAP (SHapley Additive exPlanations), a game theory approach developed to explain the contribution of each feature to the final output of any machine learning model. SHAP values provide both global and local interpretability, meaning that we can assess both how much each predictor and each observation, respectively, contribute to the performance of the classifier.
The local explanations are based on assigning a numerical measure
of credit to each input feature. Then, global model insights can be obtained by combining many local explanations from the samples [1]}.
As mentioned by the authors, the classic Shapley
values can be considered “optimal” in the sense that within a large class of approaches, they are the only way
to measure feature importance while maintaining several natural properties from cooperative game theory [2]}.
SHAP's output helps to understand the general behaviour of our model by assessing the impact of each input feature in the final decision, thus enhancing the usefulness of our framework.
